from typing import Optional
import numpy as np
import pandas as pd
import xarray as xr
from imod.mf6.dis import StructuredDiscretization
from imod.mf6.wel import WellDisStructured
from imod.msw.fixed_format import VariableMetaData
from imod.msw.pkgbase import MetaSwapPackage
[docs]
class CouplerMapping(MetaSwapPackage):
"""
This contains the data to connect MODFLOW 6 cells to MetaSWAP svats.
This class is responsible for the file `mod2svat.inp`. It also includes
connection to wells.
Parameters
----------
modflow_dis: StructuredDiscretization
Modflow 6 structured discretization
well: WellDisStructured (optional)
If given, this parameter describes sprinkling of SVAT units from MODFLOW
cells.
"""
_file_name = "mod2svat.inp"
_metadata_dict = {
"mod_id": VariableMetaData(10, 1, 9999999, int),
"free": VariableMetaData(2, None, None, str),
"svat": VariableMetaData(10, 1, 9999999, int),
"layer": VariableMetaData(5, 0, 9999, int),
}
_with_subunit = ("mod_id",)
_without_subunit = ()
_to_fill = ("free",)
[docs]
def __init__(
self,
modflow_dis: StructuredDiscretization,
well: Optional[WellDisStructured] = None,
):
super().__init__()
self.well = well
# Test if equal or larger than 1, to ignore idomain == -1 as well. Don't
# assign to self.dataset, as grid extent might differ from svat when
# MetaSWAP only covers part of the Modflow grid domain.
self.idomain_active = modflow_dis["idomain"] >= 1
def _create_mod_id_rch(self, svat):
"""
Create modflow indices for the recharge layer, which is where
infiltration will take place.
"""
self.dataset["mod_id"] = xr.full_like(svat, fill_value=0, dtype=np.int64)
n_subunit = svat["subunit"].size
idomain_top_active = self.idomain_active.sel(layer=1, drop=True)
n_mod_top = idomain_top_active.sum()
# idomain does not have a subunit dimension, so tile for n_subunits
mod_id_1d = np.tile(np.arange(1, n_mod_top + 1), (n_subunit, 1))
self.dataset["mod_id"].values[:, idomain_top_active.values] = mod_id_1d
def _render(self, file, index, svat):
self._create_mod_id_rch(svat)
# package check only possible after calling _create_mod_id_rch
self._pkgcheck()
data_dict = {"svat": svat.values.ravel()[index]}
data_dict["layer"] = np.full_like(data_dict["svat"], 1)
for var in self._with_subunit:
data_dict[var] = self._index_da(self.dataset[var], index)
# Get well values
if self.well:
mod_id_well, svat_well, layer_well = self._create_well_id(svat)
data_dict["mod_id"] = np.append(mod_id_well, data_dict["mod_id"])
data_dict["svat"] = np.append(svat_well, data_dict["svat"])
data_dict["layer"] = np.append(layer_well, data_dict["layer"])
for var in self._to_fill:
data_dict[var] = ""
dataframe = pd.DataFrame(
data=data_dict, columns=list(self._metadata_dict.keys())
)
self._check_range(dataframe)
return self.write_dataframe_fixed_width(file, dataframe)
def _create_well_id(self, svat):
"""
Get modflow indices, svats, and layer number for the wells
"""
n_subunit = svat["subunit"].size
# Convert to Python's 0-based index
well_row = self.well["row"] - 1
well_column = self.well["column"] - 1
well_layer = self.well["layer"] - 1
n_mod = self.idomain_active.sum()
mod_id = xr.full_like(self.idomain_active, 0, dtype=np.int64)
mod_id.values[self.idomain_active.values] = np.arange(1, n_mod + 1)
well_mod_id = mod_id[well_layer, well_row, well_column]
well_mod_id = np.tile(well_mod_id, (n_subunit, 1))
well_svat = svat.values[:, well_row, well_column]
well_active = well_svat != 0
well_svat_1d = well_svat[well_active]
well_mod_id_1d = well_mod_id[well_active]
# Tile well_layers for each subunit
layer = np.tile(well_layer + 1, (n_subunit, 1))
layer_1d = layer[well_active]
return (well_mod_id_1d, well_svat_1d, layer_1d)
